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My thoughts not yours...Wed, 21 Feb 2018 20:08:11 +0000en-UShourly1https://wordpress.org/?v=4.9.3Broadcom cuts Qualcomm offer to $117 billion after new NXP dealhttps://www.webpirates.co.uk/2018/broadcom-cuts-qualcomm-offer-to-117-billion-after-new-nxp-deal
https://www.webpirates.co.uk/2018/broadcom-cuts-qualcomm-offer-to-117-billion-after-new-nxp-deal#respondWed, 21 Feb 2018 20:08:11 +0000http://www.reuters.com/article/us-qualcomm-m-a-broadcom/broadcom-cuts-qualcomm-offer-to-117-billion-after-new-nxp-deal-idUSKCN1G51M3?feedType=RSS&feedName=technologyNews
Qualcomm responded that Broadcom had made “an inadequate offer even worse,” setting up a showdown on March 6, when Qualcomm shareholders are scheduled to elect an 11-member board and decide whether to hand control to a slate of ...]]>(Reuters) – Microchip maker Broadcom Ltd (AVGO.O) cut its bid for Qualcomm Inc (QCOM.O) on Wednesday by 4 percent to $117 billion as it objected to Qualcomm’s decision to raise its own bid for NXP Semiconductors NV (NXPI.O) to $44 billion.

Qualcomm responded that Broadcom had made “an inadequate offer even worse,” setting up a showdown on March 6, when Qualcomm shareholders are scheduled to elect an 11-member board and decide whether to hand control to a slate of six nominees put forward by Broadcom.

A tie-up between Broadcom and Qualcomm would be the biggest technology acquisition ever. The takeover battle is at the heart of a race to consolidate the wireless technology equipment sector, as smartphone makers such as Apple Inc (AAPL.O) and Samsung Electronics Co Ltd (005930.KS) use their market dominance to negotiate lower chip prices.

Broadcom said that Qualcomm’s raised bid would amount to overpaying for NXP and cut its own bid as a result.

“Qualcomm’s board acted against the best interests of its stockholders,” Broadcom said in its statement on Wednesday.

The moves raise the chances that Qualcomm will buy NXP and lowered the chances of a Broadcom-Qualcomm deal, CFRA Research analyst Scott Kessler wrote.

Broadcom also criticized Qualcomm on Wednesday for not liaising with it before raising its NXP bid, as proxy advisory firm Institutional Shareholder Services Inc had recommended.

FILE PHOTO: A sign on the Qualcomm campus is seen in San Diego, California, U.S. November 6, 2017. REUTERS/Mike Blake/File Photo

Qualcomm on Wednesday defended the value of the NXP deal, which it argued would expand Qualcomm’s markets and offer cost savings, and said Broadcom had refused to engage in discussions on the value of Broadcom’s bid for Qualcomm. The deal with Broadcom continued to face a “highly uncertain path to regulatory approvals,” Qualcomm added.

Qualcomm shares were trading down 0.5 percent at $63.53 on Wednesday afternoon in New York, while Broadcom shares were up 1.2 percent at $252.69.

Qualcomm raised its offer for NXP from $110 to $127.50 per share in cash on Tuesday.

In exchange, it received binding agreements from nine NXP stockholders that collectively own more than 28 percent of NXP’s outstanding shares to support the deal. These include hedge funds Elliott Advisors (UK) Ltd and Soroban Capital Partners LP, which had pushed for a higher price.

Broadcom’s latest $82 per share cash-and-stock offer for Qualcomm was contingent on it buying NXP at its earlier offered price of $110 per share. Broadcom said on Wednesday that if the NXP deal goes ahead at $127.50, it will reduce the cash portion of its bid for Qualcomm by $3 per share.

Qualcomm is contractually obligated to complete the NXP deal, so that acquisition could fall apart only if too few NXP shareholders support the tender offer or if China’s MOFCOM, the only relevant regulator that has yet to approve the deal, blocks it.

Under the new terms agreed with NXP’s board, the deal with Qualcomm is contingent on 70 percent of NXP’s shares being tendered, instead of the 80 percent threshold required in the earlier agreement signed in October 2016. Once this threshold is reached, Qualcomm can take over the entire company through a “second-step” transaction mechanism.

NXP shares were trading at $125.70 on Wednesday, close to the new deal price, indicating that most investors think the acquisition will go through.

Broadcom said on Wednesday other conditions of the proposed merger agreement remained unchanged, including an $8 billion breakup fee to be paid to Qualcomm should regulators thwart the deal.

Reporting by Greg Roumeliotis in New York; Additional reporting by Supantha Mukherjee and Munsif Vengattil in BengaluruEditing by Tom Brown and Matthew Lewis

]]>https://www.webpirates.co.uk/2018/broadcom-cuts-qualcomm-offer-to-117-billion-after-new-nxp-deal/feed0AI software differentiates between extremist propaganda and newshttps://www.webpirates.co.uk/2018/ai-software-differentiates-between-extremist-propaganda-and-news
https://www.webpirates.co.uk/2018/ai-software-differentiates-between-extremist-propaganda-and-news#respondWed, 21 Feb 2018 13:44:54 +0000http://403087901
Reuters, the news and media division of Thomson Reuters, is the world’s largest international multimedia news provider reaching more than one billion people every day. Reuters provides trusted business, financial, national, and international news to professionals via Thomson Reuters desktops, the world’s media organizations, and directly to consumers at Reuters.com and via Reuters TV. Learn more about Thomson Reuters products:
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Reuters, the news and media division of Thomson Reuters, is the world’s largest international multimedia news provider reaching more than one billion people every day. Reuters provides trusted business, financial, national, and international news to professionals via Thomson Reuters desktops, the world’s media organizations, and directly to consumers at Reuters.com and via Reuters TV. Learn more about Thomson Reuters products:

]]>https://www.webpirates.co.uk/2018/ai-software-differentiates-between-extremist-propaganda-and-news/feed0The GANfather: The man who’s given machines the gift of imaginationhttps://www.webpirates.co.uk/2018/the-ganfather-the-man-whos-given-machines-the-gift-of-imagination
https://www.webpirates.co.uk/2018/the-ganfather-the-man-whos-given-machines-the-gift-of-imagination#respondWed, 21 Feb 2018 12:00:00 +0000https://www.technologyreview.com/s/610253/the-ganfather-the-man-whos-given-machines-the-gift-of-imagination/
Researchers were already using neural networks, algorithms loosely modeled on the web of neurons in the human brain, as “generative” models to create plausible new data of ...]]>One night in 2014, Ian Goodfellow went drinking to celebrate with a fellow doctoral student who had just graduated. At Les 3 Brasseurs (The Three Brewers), a favorite Montreal watering hole, some friends asked for his help with a thorny project they were working on: a computer that could create photos by itself.

Researchers were already using neural networks, algorithms loosely modeled on the web of neurons in the human brain, as “generative” models to create plausible new data of their own. But the results were often not very good: images of a computer-generated face tended to be blurry or have errors like missing ears. The plan Goodfellow’s friends were proposing was to use a complex statistical analysis of the elements that make up a photograph to help machines come up with images by themselves. This would have required a massive amount of number-crunching, and Goodfellow told them it simply wasn’t going to work.

In the future, computers will get much better at feasting on raw data and working out what they need to learn from it.

But as he pondered the problem over his beer, he hit on an idea. What if you pitted two neural networks against each other? His friends were skeptical, so once he got home, where his girlfriend was already fast asleep, he decided to give it a try. Goodfellow coded into the early hours and then tested his software. It worked the first time.

What he invented that night is now called a GAN, or “generative adversarial network.” The technique has sparked huge excitement in the field of machine learning and turned its creator into an AI celebrity.

Christie Hemm Klok

In the last few years, AI researchers have made impressive progress using a technique called deep learning. Supply a deep-learning system with enough images and it learns to, say, recognize a pedestrian who’s about to cross a road. This approach has made possible things like self-driving cars and the conversational technology that powers Alexa, Siri, and other virtual assistants.

That will mark a big leap forward in what is known in AI as “unsupervised learning.”

But while deep-learning AIs can learn to recognize things, they have not been good at creating them. The goal of GANs is to give machines something akin to an imagination.

Doing so wouldn’t merely enable them to draw pretty pictures or compose music; it would make them less reliant on humans to instruct them about the world and the way it works. Today, AI programmers often need to tell a machine exactly what’s in the training data it’s being fed—which of a million pictures contain a pedestrian crossing a road, and which don’t. This is not only costly and labor-intensive; it limits how well the system deals with even slight departures from what it was trained on. In the future, computers will get much better at feasting on raw data and working out what they need to learn from it without being told.

That will mark a big leap forward in what’s known in AI as “unsupervised learning.” A self-driving car could teach itself about many different road conditions without leaving the garage. A robot could anticipate the obstacles it might encounter in a busy warehouse without needing to be taken around it.

Our ability to imagine and reflect on many different scenarios is part of what makes us human. And when future historians of technology look back, they’re likely to see GANs as a big step toward creating machines with a human-like consciousness. Yann LeCun, Facebook’s chief AI scientist, has called GANs “the coolest idea in deep learning in the last 20 years.” Another AI luminary, Andrew Ng, the former chief scientist of China’s Baidu, says GANs represent “a significant and fundamental advance” that’s inspired a growing global community of researchers.

The GANfather, Part II: AI fight club

Goodfellow is now a research scientist on the Google Brain team, at the company’s headquarters in Mountain View, California. When I met him there recently, he still seemed surprised by his superstar status, calling it “a little surreal.” Perhaps no less surprising is that, having made his discovery, he now spends much of his time working against those who wish to use it for evil ends.

The magic of GANs lies in the rivalry between the two neural nets. It mimics the back-and-forth between a picture forger and an art detective who repeatedly try to outwit one another. Both networks are trained on the same data set. The first one, known as the generator, is charged with producing artificial outputs, such as photos or handwriting, that are as realistic as possible. The second, known as the discriminator, compares these with genuine images from the original data set and tries to determine which are real and which are fake. On the basis of those results, the generator adjusts its parameters for creating new images. And so it goes, until the discriminator can no longer tell what’s genuine and what’s bogus.

A GAN trained on photos of real celebrities came up with its own set of imaginary stars. In most cases, the fakes looked pretty realistic.

In one widely publicized example last year, researchers at Nvidia, a chip company heavily invested in AI, trained a GAN to generate pictures of imaginary celebrities by studying real ones. Not all the fake stars it produced were perfect, but some were impressively realistic. Unlike other machine-learning approaches that require tens of thousands of training images, GANs can become proficient with a few hundred.

This power of imagination is still limited. Once it’s been trained on a lot of dog photos, a GAN can generate a convincing fake image of a dog that has, say, a different pattern of spots; but it can’t conceive of an entirely new animal. The quality of the original training data also has a big influence on the results. In one telling example, a GAN began producing pictures of cats with random letters integrated into the images. Because the training data contained cat memes from the internet, the machine had taught itself that words were part of what it meant to be a cat.

Getting GANS to work well can be tricky. If there are glitches, the results can be bizarre.

GANs are also temperamental, says Pedro Domingos, a machine-learning researcher at the University of Washington. If the discriminator is too easy to fool, the generator’s output won’t look realistic. And calibrating the two dueling neural nets can be difficult, which explains why GANs sometimes spit out bizarre stuff such as animals with two heads.

Still, the challenges haven’t deterred researchers. Since Goodfellow and a few others published the first study on his discovery, in 2014, hundreds of GAN-related papers have been written. One fan of the technology has even created a web page called the “GAN zoo,” dedicated to keeping track of the various versions of the technique that have been developed.

The most obvious immediate applications are in areas that involve a lot of imagery, such as video games and fashion: what, for instance, might a game character look like running through the rain? But looking ahead, Goodfellow thinks GANs will drive more significant advances. “There are a lot of areas of science and engineering where we need to optimize something,” he says, citing examples such as medicines that need to be more effective or batteries that must get more efficient. “That’s going to be the next big wave.”

In high-energy physics, scientists use powerful computers to simulate the likely interactions of hundreds of subatomic particles in machines like the Large Hadron Collider at CERN in Switzerland. These simulations are slow and require massive computing power. Researchers at Yale University and Lawrence Berkeley National Laboratory have developed a GAN that, after training on existing simulation data, learns to generate pretty accurate predictions of how a particular particle will behave, and does it much faster.

Goodfellow’s creation can be used to imagine all sorts of things, including new interior designs.

Medical research is another promising field. Privacy concerns mean researchers sometimes can’t get enough real patient data to, say, analyze why a drug didn’t work. GANs can help solve this problem by generating fake records that are almost as good as the real thing, says Casey Greene of the University of Pennsylvania. This data could be shared more widely, helping to advance research, while the real records are tightly protected.

The GANfather, Part III: Bad fellows

There is a darker side, however. A machine designed to create realistic fakes is a perfect weapon for purveyors of fake news who want to influence everything from stock prices to elections. AI tools are already being used to put pictures of other people’s faces on the bodies of porn stars and put words in the mouths of politicians. GANs didn’t create this problem, but they’ll make it worse.

Hany Farid, who studies digital forensics at Dartmouth College, is working on better ways to spot fake videos, such as detecting slight changes in the color of faces caused by inhaling and exhaling that GANs find hard to mimic precisely. But he warns that GANs will adapt in turn. “We’re fundamentally in a weak position,” says Farid.

This cat-and-mouse game will play out in cybersecurity, too. Researchers are already highlighting the risk of “black box” attacks, in which GANs are used to figure out the machine-learning models with which plenty of security programs spot malware. Having divined how a defender’s algorithm works, an attacker can evade it and insert rogue code. The same approach could also be used to dodge spam filters and other defenses.

“There are a lot of areas of science and engineering where we need to optimize something. That’s going to be the next big wave.”

Goodfellow is well aware of the dangers. Now heading a team at Google that’s focused on making machine learning more secure, he warns that the AI community must learn the lesson of previous waves of innovation, in which technologists treated security and privacy as an afterthought. By the time they woke up to the risks, the bad guys had a significant lead. “Clearly, we’re already beyond the start,” he says, “but hopefully we can make significant advances in security before we’re too far in.”

Nonetheless, he doesn’t think there will be a purely technological solution to fakery. Instead, he believes, we’ll have to rely on societal ones, such as teaching kids critical thinking by getting them to take things like speech and debating classes. “In speech and debate you’re competing against another student,” he says, “and you’re thinking about how to craft misleading claims, or how to craft correct claims that are very persuasive.” He may well be right, but his conclusion that technology can’t cure the fake-news problem is not one many will want to hear.

]]>https://www.webpirates.co.uk/2018/the-ganfather-the-man-whos-given-machines-the-gift-of-imagination/feed0Computer shops embrace lucrative business: outfitting cryptocurrency minershttps://www.webpirates.co.uk/2018/computer-shops-embrace-lucrative-business-outfitting-cryptocurrency-miners
https://www.webpirates.co.uk/2018/computer-shops-embrace-lucrative-business-outfitting-cryptocurrency-miners#respondWed, 21 Feb 2018 03:16:00 +0000http://www.reuters.com/article/us-crypto-currencies-mining-analysis/computer-shops-embrace-lucrative-business-outfitting-cryptocurrency-miners-idUSKCN1G502L?feedType=RSS&feedName=technologyNews
Scores of miners from around the world are traveling to places like Hong Kong’s Sham Shui Po and Singapore’s Sim Lim Square to buy the rigs, which the shops’ hardware geeks expertly build behind counters in their cramped boutiques.
Some miners only buy components: a motherboard, graphic processing units, fans, power ...]]>HONG KONG/SINGAPORE (Reuters) – Some of the biggest electronics bazaars in Asia are being flooded with customers looking for the latest piece of technology: cryptocurrency mining rigs.

Scores of miners from around the world are traveling to places like Hong Kong’s Sham Shui Po and Singapore’s Sim Lim Square to buy the rigs, which the shops’ hardware geeks expertly build behind counters in their cramped boutiques.

Some miners only buy components: a motherboard, graphic processing units, fans, power adapters, a display card and a memory card. But even if the vendors assemble them on the spot for a small fee, the finished product is usually still a relative bargain.

“It’s 30-50 percent cheaper to buy equipment related to cryptomining in Hong Kong than in Europe,” Russian bitcoin miner Dima Popov said. Hong Kong, for instance, has no sales tax and is closer to component suppliers.

Popov buys display cards, motherboards and power supplies in Hong Kong and mines cryptocurrencies in Russia, where electricity is cheaper and the climate is more suitable.

The demand for the rigs has added a new dimension to Asia’s tech shopping hubs whose once bustling business fizzled out in recent years hit by waning demand for personal computers. Storefronts that once catered mostly to locals, selling phones and other consumer gear, are now greeting foreign visitors searching for hardware that might make them rich.

DIGITAL MINING

The rigs are often stacked in warehouses as large as airplane hangars and are monitored constantly.

Each unit contains energy-intensive processors that solve complex mathematical computations. When they do so, they are awarded them the right to validate a blockchain transaction, earning them a “mining” fee.

One cryptocurrency expert said anecdotal evidence suggests on average miners would get their money back in about three months. But those with small, home-based operations might have to wait much longer for a payoff.

In Hong Kong, shopkeepers say most buyers come from Russia, but they have had clients from western Europe, Africa and South Korea. Singapore sees visitors from neighboring countries with cheaper operation costs.

Their mining machines’ parts are mostly manufactured in China using chips from Advanced Micro Devices (AMD) (AMD.O) and Nvidia (NVDA.O), who are looking for export customers amid fears that Beijing will crack down on cryptocurrency miners.

“We’ve been selling more these few months and we often run out of stock” as miners move elsewhere and components flood out of China, said Grant Mak of C. Base Computer.

CHIPMAKERS CAPITALIZE

As miners hunt for deals on gear, chipmakers see opportunity. Rigs can cost anywhere from a few thousand dollars to tens of thousands or more – and processing chips are the priciest component.

Samsung Electronics (005930.KS), the world’s biggest microchip producer, said “explosive” demand for the graphics processors used in mining cryptocurrencies is driving fresh growth.

“A share of clients from the virtual currency industry is expected to grow dramatically this year in our foundry customer base,” Lee Sang-hyun, vice president of Samsung’s foundry business, said during a conference call.

Taiwan’s Advanced Semiconductor Engineering predicted an upswing in demand this year for chips used for mining rigs.

And Taiwan Semiconductor Manufacturing Co Ltd (TSMC) (2330.TW), the world’s No.1 foundry by revenue and volume, has embraced cryptocurrency mining as a business venture.

But they know how fast that could change.

“The urge to mine cryptocurrency is very strong. The incentive, of course, depends on the price of cryptocurrency. And the price of cryptocurrency is very volatile. But the demand right now or for the last year has been very strong, and we expect it to continue to be strong,” TSMC’s chairman, Morris Chang, said on an earnings call last month.

‘THEY TRUST SINGAPORE’

Singapore’s smaller version of Sham Shui Po, the electronics shopping center at Sim Lim Square, is also seeing increased demand for mining equipment.

Anuj Agarwal, a 39-year-old consultant for his brother’s shop, Bizgram, said he has dealt with buyers from Vietnam, Malaysia, Indonesia and Russia.

“Foreigners come to Singapore as there is immediate supply of mining rigs here, and they trust Singapore as a country,” he said, adding that some were as young as 16 and came with their parents.

Nearby at Video-Pro, Liu Xiao Yu said he cannot keep up with the demand.

“There was a customer who asked for a rig with 500 GPU cards, which amounted to over S$350,000 ($262,700),” Liu said. “There was another who came by last week asking for 1,000 GPU cards, but I am afraid to accept the offer as supplies are low now.”

One rig with GPUs usually has six to 12 such cards.

Vendors say the 60 percent drop from an all-time high of close to $20,000 BTC=BTSP did not deter their regular customers, who tend to own large mining operations.

“Individual players may be freaked out, but the big players do not really mind. Big players are our major customers,” said Roy Chan, shop manager of BNW Technology in Hong Kong.

In Singapore, some sellers saw a 40 percent drop in revenue as Bitcoin prices tanked, but the shops are unfazed.

“Once the value of Bitcoin increases again, we will receive multiple calls and emails from customers all over the world,” Agarwal from Bizgram said.

Reporting by Wyman Ma in HONG KONG and Dewey Sim in SINGAPORE; and Ju-Min Park in SEOUL. Additional reporting by the SHANGHAI newsroom. Writing by Clare Jim; Editing by Marius Zaharia and Gerry Doyle